Applying Data Preprocessing Methods to Predict Premature Birth.

Data mining and pattern classification tools have{enabled prediction of several medical outcomes with high levels of accuracy. This is due to the capability of handling large datasets, even those with missing values. Preterm birth (PTB) can have damaging long-term effects for infants and rates have been increasing over the last two decades worldwide. The purpose of this work was to investigate whether preprocessing methods, when applied to two different prenatal datasets, can improve prediction accuracy of our software tool to predict PTB. The primary software used within this work was R. The software was used to deal with missing values and class imbalances found in these two datasets. The results show that in comparison to our past work, we have managed to increase the performance of the prediction tool using the metrics of sensitivity, specificity, and ROC values.

Investigation into the past and future of women in science and engineering.

Covering the Ancient Greek era, the Middle Ages, the Renaissance, the Enlightenment, the 19th and 20th C., this paper explores the visions of the abilities of women, their access to education, and their roles in these epochs. Recent data on the participation rate of women in science and engineering, the culture in these fields, and strategies to increase their presence are discussed. The paper ends with a discussion on how science and engineering could benefit from integrating and valuing a blend of masculine and feminine perspectives. Biomedical engineering as a field frequently chosen by women is mentioned.

The relationship between intellectual disability, Indigenous status and risk of reoffending in juvenile offenders on community orders.

BACKGROUND: Intellectual disability (ID), age and aboriginal status have been independently implicated as risk factors for offending to varying degrees. This study examined the relationship between age, ID and the Indigenous status of juvenile offenders. It also examined the outcomes of the sample's offending in terms of court appearances and sentencing, criminogenic needs and risk of reoffending. METHOD: The sample comprised 800 juvenile offenders on community orders of whom 19% were Indigenous, who completed the New South Wales Young People on Community Order Health Survey between 2003 and 2005. Risk and criminogenic needs were evaluated using the Youth Level of Service/Case Management Inventory (Australian Adaptation) (YLS/CMI: AA). RESULTS: Those with an ID were found to have a higher risk of reoffending than those without an ID. Those with an ID were also more likely to be younger and Indigenous. For Indigenous young offenders, there was no difference between those with and without an ID in risk category allocation or number of court dates. For non-Indigenous young offender, those with an ID had higher risk scores and more court dates. CONCLUSIONS: This study provided evidence that Indigenous status may play a significant role in the relationship between ID and offending in juvenile offenders on community orders. These findings have clear implications for the 'risk', 'needs' and 'responsivity' principles of offender classification for treatment. Emphasis is placed on the requirement for addressing the needs of Indigenous juvenile offenders with an ID.

Bayesian-based diagnostic decision-support for pediatrics.

The goal of this project was to develop a Pediatric Decision Support system (PDS) that allows a resident physician to define a patient case based on symptoms (diagnostic signs and test results) and generates a list of possible diagnoses based on the World Health Organization's International Classification of Diseases (ICD10). The intent is to improve the diagnostic approach taken by resident physicians and eventually become a training tool in medical education programs.

Segmentation and landmark identification in infrared images of the human body.

The segmentation and landmark identification in infrared images of the human body are key steps in a computerized processing of large database of thermal images. The segmentation task is especially challenging due to specific characteristics of thermal images. Few papers deal with segmentation techniques for clinical infrared images and available segmentation methods (e.g. for breast or military thermal images) do not perform well on other types of images. This paper presents a few strategies for the automated segmentation and registration of anatomical landmarks on thermal images of arms and hands. The segmentation method is based on mathematical morphological operations and simple rule based processing easily available through prior knowledge about the objects of interest.

An Analysis Of Chirpp Data To Predict Severe ATV Injuries Using Artificial Neural Networks.

This paper describes the development of a tool to predict the severity of all-terrain vehicle (ATV) injuries using artificial neural networks (ANNs). The data was obtained from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP). The main objective of the study was to identify the contribution of input variables in predicting severe injury or death. An ANN architecture with 9 hidden nodes and one hidden layer resulted in optimal performance: a logarithmic-sensitivity index of 0.099, sensitivity of 47.3%, specificity of 80.8%, correct classification rate (CCR) of 68.6% and receiver operating curve (ROC) area of 0.711. The minimum data set that can help predict injury severity is discussed.

Evolution of the surface temperature of pianists' arm muscles using infrared thermography.

Musculoskeletal disorders are very frequent among musicians. Diagnosis is difficult due to the lack of objective tests and the multiplicity of symptoms. Treatment is also problematic and often requires that the musician stop playing. Most of these disorders are inflammatory in nature, and therefore involve temperature changes in the affected regions. Temperature measurements were recorded with an infrared camera. In this paper we present an overview of the temperature measurements made in the arms of 8 pianists during regular piano practice sessions.

Predicting clinical outcomes for newborns using two artificial intelligence approaches.

Two different approaches, based on artificial neural networks (ANN) and fuzzy logic, were used to predict a number of outcomes of newborns: How they would be delivered, their 5 minute Apgar score, and neonatal mortality. The goal was to assess whether the methods would be comparable or whether they would perform differently for different outcomes. The results were comparable for Correct Classification Rate (CCR) and Specificity (true negative cases). Sensitivity (true positive cases) was slightly higher for the back-propagation feed-forward ANN than using the Fuzzy-Logic Classifier (FLC). Since this is one single database and a very large one, it is possible that the FLC would perform better than the ANN for very small databases, as shown by some of the co-authors in the past. The next step will be to test a small database with both methods to assess strengths and weaknesses with the intent to use both if needed with some medical data in the future.

Towards a web services infrastructure for perinatal, obstetrical, and neonatal clinical decision support.

This paper presents the design of a unifying infrastructure for clinical decision support systems (CDSSs) and medical data relating to the perinatal life cycle. The diverse CDSSs designed for deployment within the perinatal life cycle to improve care, such as Artificial Neural Networks and Case-Based Reasoners, are integrated using the eXtended Markup Language (XML) and are subsequently offered as a secure web service. These web services are accessible from anywhere within the hospital information system and from remote authorized sites. The goal of such an infrastructure is to provide integrated CDSS processing in a complex distributed environment, in order to support real-time physician decision-making. This design provides a novel web services infrastructure implementation and offers a strong case study for deploying and evaluating the web services paradigm within a health care environment.

Towards ethical decision support and knowledge management in neonatal intensive care.

Recent studies in neonatal medicine, clinical nursing, and cognitive psychology have indicated the need to augment current decision-making practice in neonatal intensive care units with computerized, intelligent decision support systems. Rapid progress in artificial intelligence and knowledge management facilitates the design of collaborative ethical decision-support tools that allow clinicians to provide better support for parents facing inherently difficult choices, such as when to withdraw aggressive treatment. The appropriateness of using computers to support ethical decision-making is critically analyzed through research and literature review. In ethical dilemmas, multiple diverse participants need to communicate and function as a team to select the best treatment plan. In order to do this, physicians require reliable estimations of prognosis, while parents need a highly useable tool to help them assimilate complex medical issues and address their own value system. Our goal is to improve and structuralize the ethical decision-making that has become an inevitable part of modern neonatal care units. The paper contributes to clinical decision support by outlining the needs and basis for ethical decision support and justifying the proposed development efforts.

Analysis of breast thermography with an artificial neural network.

Thermal imaging has been used for early breast cancer detection and risk prediction since the sixties. Examining thermograms for abnormal hyperthermia and hyper-vascularity patterns related to tumor growth is done by comparing images of contralateral breasts. Analysis can be tedious and challenging if the differences are subtle. The advanced computer technology available today can be utilized to automate the analysis and assist in decision-making. In our study, computer routines were used to perform ROI identification and image segmentation of infrared images recorded from 19 patients. Asymmetry analysis between contralateral breasts was carried out to generate statistics that could be used as input parameters to a backpropagation ANN. A simple 1-1-1 network was trained and employed to predict clinical outcomes based on the difference statistics of mean temperature and standard deviation. Results comparing the ANN output with actual clinical diagnosis are presented. Future work will focus on including more patients and more input parameters in the analysis. Performance of ANN network can be studied to select a set of parameters that would best predict the presence of breast cancer.

Automated image segmentation for breast analysis using infrared images.

In order to realize a fully automated thermogram analysis package for breast cancer detection, it is necessary to identify the region of interest in the thermal image prior to analysis. A nearly fully automated approach is outlined that is able to successfully locate the breast regions in most of the images analyzed. The approach consists of a sequence of Canny edge detectors to determine the body boundaries and to isolate the most likely candidates for the bottom breast boundary. Three different strategies for identifying the bottom breast boundary are investigated: a variation of the Hough transform to identify the curved edges in the image, an algorithm used to detect the longest connected edges that are not part of the body boundary, and a third approach involving the density of detected edges in the breast region. The last two methods show great promise in successfully segmenting the breasts.

Influence of missing values on artificial neural network performance.

The problem of databases containing missing values is a common one in the medical environment. Researchers must find a way to incorporate the incomplete data into the data set to use those cases in their experiments. Artificial neural networks (ANNs) cannot interpret missing values, and when a database is highly skewed, ANNs have difficulty identifying the factors leading to a rare outcome. This study investigates the impact on ANN performance when predicting neonatal mortality of increasing the number of cases with missing values in the data sets. Although previous work using the Canadian Neonatal Intensive Care Unit (NICU) Network s database showed that the ANN could not correctly classify any patients who died when the missing values were replaced with normal or mean values, this problem did not arise as expected in this study. Instead, the ANN consistently performed better than the constant predictor (which classifies all cases as belonging to the outcome with the highest training set a priori probability) with a 0.6-1.3% improvement over the constant predictor. The sensitivity of the models ranged from 14.5-20.3% and the specificity ranged from 99.2- 99.7%. These results indicate that nearly 1 in 5 babies who will eventually die are correctly classified by the ANN, and very few babies were incorrectly identified as patients who will die. These findings are important for patient care, counselling of parents and resource allocation.

Clinical decision support systems for intensive care units: using artificial neural networks.

The paper provides an overview of applications of artificial neural networks (ANNs) to various medical problems, with a particular focus on the intensive care unit environment (ICU). Several technical approaches were tested to see whether they improve the ANN performance in estimating medical outcomes and resource utilization in adult ICUs. These experiments include: (1) use of the weight-elimination cost function; (2) use of 'high' and 'low' nodes for input variables; (3) verifying the effect of the total number of input variables on the results; (4) testing the impact of the value of the constant predictor on the performance of the ANNs. The developments presented intend to help medical and nursing personnel to assess patient status, assist in making a diagnosis, and facilitate the selection of a course of therapy.

Selective sampling to overcome skewed a priori probabilities with neural networks.

Highly skewed a priori probabilities present challenges for researchers developing medical decision aids due to a lack of information on the rare outcome of interest. This paper attempts to overcome this obstacle by artificially increasing the mortality rate of the training sets. A weight pruning technique called weight-elimination is also applied to this coronary artery bypass grafting (CABG) database to assess its impact on the artificial neural network's (ANN) performance. The results showed that increasing the mortality rate improved the sensitivity rates at the cost of the other performance measures, and the weight-elimination cost function improved the sensitivity rate without seriously affecting the other performance measures.

Decision-support and intelligent tutoring systems in medical education.

One of the challenges in medical education is to teach the decision-making process. This learning process varies according to the experience of the student and can be supported by various tools. In this paper we present several approaches that can strengthen this mechanism, from decision-support tools, such as scoring systems, Bayesian models, neural networks, to cognitive models that can reproduce how the students progressively build their knowledge into memory and foster pedagogic methods.

Clinical decision-support systems for intensive care units using case-based reasoning.

The artificial intelligence approach used in this work focusses on case-based reasoning techniques for the estimation of medical outcomes and resource utilization. The systems were designed with a view to help medical and nursing personnel to assess patient status, assist in making a diagnosis, and facilitate the selection of a course of therapy. The initial prototype provided information on the closest-matching patient cases to the newest patient admission in an adult intensive care unit (ICU). The system was subsequently re-designed for use in a neonatal ICU. The results of a short clinical pilot evaluation performed in both adult and neonatal units are reported and have led to substantial improvement of the prototype. Future work will include longer-term clinical trials for both adult and neonatal ICUs, once all the software changes have been made to both prototypes in response to the comments of the users made during the preliminary evaluations. To date, the results are very encouraging and physician interest in the potential clinical usefulness of these two systems remains high, and particularly so in the new testing environment in Ottawa.

New advances and validation of knowledge management tools for critical care using classifier techniques.

An earlier version (2.0) of the case-based reasoning (CBR) tool, called IDEAS for ICU's, allowed users to compare the ten closest matching cases to the newest patient admission, using a large database of intensive care patient records, and physician-selected matching-weights [1,2]. The new version incorporates matching-weights, which have been determined quantitatively. A faster CBR matching engine has also been incorporated into the new CBR. In a second approach, a back-propagation, feed-forward artificial neural network estimated two classes of the outcome "duration of artificial ventilation" for a subset of the database used for the CBR work. Weight-elimination was successfully applied to reduce the number of input variables and speed-up the estimation of outcomes. New experiments examined the impact of using a different number of input variables on the performance of the ANN, measured by correct classification rates (CCR) and the Average Squared Error (ASE).

Computer-assisted decision support systems for patient management in an intensive care unit.

The application of the intelligent monitoring techniques of case-based reasoning and neural network analysis to physician decision making concerning patient care in an Intensive Car Unit (ICU) is described. Case-based reasoning offers a model for quickly matching--using a predetermined hierarchical structure--a single patient's parameters (text or numeric) to similar parameters contained in a clinical database. The output produces a group of patients which may be set to match exactly on certain characteristics and may also be set to match "as closely as possible" on a gradient of patient properties. Clinicians may thus use the system to find the group of the closest matching cases to their current patient. Aspects of the ICU history of the selected group may then be displayed graphically (e.g., mortality, length of stay, hours of ventilation, procedures utilized, and complications encountered). Neural network analysis is a pattern recognition technique which uses a training set of patient data (text or numeric) to seek mathematical relationships between various subsets of patient parameters. The discovered relationships from the training set are then applied to estimate the outcomes (e.g., mortality, length of stay, hours of ventilation) of new patients. The effects of these intelligent monitoring techniques are scheduled to be tested in a field trial held in a regional referral center ICU.